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Retinal image of surrounding objects varies tremendously due to the changesin position, size, pose, illumination condition, background context, occlusion,noise, and nonrigid deformations. But despite these huge variations, our visualsystem is able to invariantly recognize any object in just a fraction of asecond. To date, various computational models have been proposed to mimic thehierarchical processing of the ventral visual pathway, with limited success.Here, we show that the association of both biologically inspired networkarchitecture and learning rule significantly improves the models' performancewhen facing challenging invariant object recognition problems. Our model is anasynchronous feedforward spiking neural network. When the network is presentedwith natural images, the neurons in the entry layers detect edges, and the mostactivated ones fire first, while neurons in higher layers are equipped withspike timing-dependent plasticity. These neurons progressively become selectiveto intermediate complexity visual features appropriate for objectcategorization. The model is evaluated on 3D-Object and ETH-80 datasets whichare two benchmarks for invariant object recognition, and is shown to outperformstate-of-the-art models, including DeepConvNet and HMAX. This demonstrates itsability to accurately recognize different instances of multiple object classeseven under various appearance conditions (different views, scales, tilts, andbackgrounds). Several statistical analysis techniques are used to show that ourmodel extracts class specific and highly informative features.

Bio-inspired Unsupervised Learning of Visual Features Leads to Robust Invariant Object Recognition